Background Over the past decade there has been a growing awareness of, and interest in, the trace element concentration differences between normal and diseased tissues. Significant changes in tissue concentrations of Zinc (Zn) and Copper (Cu) have been previously reported in inflammation and cancer of certain human tissues.
Aim:(1)To correlate between Zn and Cu concentrations and the histological picture of normal and certain inflamed human tissues, namely the gall bladder (GB) the vermiform appendix (VA), visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). (2) to detect whether there is a difference in the above-mentioned parameters between VAT and SAT. (3) to obtain recordings for trace element levels in human tissues.
Methods: Diseased GB (10), VA (10), VAT (10) near these organs and SAT (10) were removed by surgery. Tissues from these organs were then processed for histopathology and analyzed for trace elements concentration by Atomic Absorption Flame-Emission Spectrophotometer.
Results: Zn concentration was high in VAT (0.410 μg/g ± 0.181), GB (0.478 μg/g ±0.531) and VA (0.419 μg/g ±0.123) when compared to its level in SAT (0.1329 μg/g ± 0.0129) and the difference was significant (<0.007,<0.056 and <0.000 respectively).
Cu concentration was high in VAT (0.640 μg/g ± 0.150) and GB (0.919 μg/g ± 0.564) when compared to SAT (0.3893 μg/g ± 0.0130) and the difference was significant (<0.005 and <0.011 respectively). Cu concentration in the VA was low (0.2055 μg/g ±0.0654) and significantly different from all the other tissues (VA vs VAT <0.000, VA vs GB <0.002 and VA vs SAT <0.000).
The histology findings were typical of chronic inflammatory reactions in the GB and of acute inflammation in the VA.
Conclusions: The increase in tissue concentrations of Zn in VAT, GB & VA is due to inflammation.
The high Cu level in chronically inflamed GB and neighboring VAT is due to the increased need for this element during inflammation.
Our results, together with findings reported by others, allow us to think of using trace elements, namely Zn and Cu, as tools for diagnosis and treatment in appropriate conditions.
<span lang="EN-US">Diabetes is one of the deadliest diseases in the world that can lead to stroke, blindness, organ failure, and amputation of lower limbs. Researches state that diabetes can be controlled if it is detected at an early stage. Scientists are becoming more interested in classification algorithms in diagnosing diseases. In this study, we have analyzed the performance of five classification algorithms namely naïve Bayes, support vector machine, multi layer perceptron artificial neural network, decision tree, and random forest using diabetes dataset that contains the information of 2000 female patients. Various metrics were applied in evaluating the performance of the classifiers such as precision, area under the c
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